Forecasting Government Bond Spreads with Heuristic Models: Evidence from the Eurozone Periphery

Filipa Da Silva Fernandes, Charalampos Stasinakis, Zivile Zekaite

    Research output: Contribution to journalArticlepeer-review

    3 Citations (Scopus)
    118 Downloads (Pure)

    Abstract

    This study investigates the predictability of European long-term government bond spreads through the application of heuristic and metaheuristic support vector regression (SVR) hybrid structures. Genetic, krill herd and sine–cosine algorithms are applied to the parameterization process of the SVR and locally weighted SVR (LSVR) methods. The inputs of the SVR models are selected from a large pool of linear and non-linear individual predictors. The statistical performance of the main models is evaluated against a random walk, an Autoregressive Moving Average, the best individual prediction model and the traditional SVR and LSVR structures. All models are applied to forecast daily and weekly government bond spreads of Greece, Ireland, Italy, Portugal and Spain over the sample period 2000–2017. The results show that the sine–cosine LSVR is outperforming its counterparts in terms of statistical accuracy, while metaheuristic approaches seem to benefit the parameterization process more than the heuristic ones.

    Original languageEnglish
    Pages (from-to)87–118
    Number of pages32
    JournalAnnals of Operations Research
    Volume282
    Issue number1-2
    Early online date15 Mar 2018
    DOIs
    Publication statusPublished - Nov 2019

    Bibliographical note

    The final publication is available at Springer via http://dx.doi.org/10.1007/s10479-018-2808-0

    Keywords

    • Government bond spreads
    • Support Vector Regression
    • Krill Herd
    • Sine Cosine Algorithm
    • Eurozone

    ASJC Scopus subject areas

    • Economics, Econometrics and Finance(all)

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